Machine Learning Engineer and Data Scientist are two of the Hottest Jobs in the Industry right now and for good reason. With 2.5 Quintillion bytes of data being generated every day, a professional who can organize this humongous data to provide business solutions is indeed the hero! The competition between Machine Learning Engineer vs Data Scientist is increasing and the line between them diminishing.

The mix of personality traits, experience, and analytic skills required for this is considered difficult to find, and, thus, the demand for qualified Data scientists and Machine Learning Engineers has exceeded supply in recent years. So, let’s begin the “Machine Learning Engineer vs Data Scientist” article to find out the differences between the two Professionals in the following order:

Who is a Data Scientist?

Although there are several definitions of Data Scientists available, basically they are professionals who practice the art of Data Science. Data Scientists crack complex data problems with their expertise in scientific disciplines. It is a position of Specialists.

They specialize in different types of skills like speech, text analytics (NLP), image and video processing, medicine and material simulation, etc. Each of these specialist roles is very limited in number and hence the value of such a specialist is immense. Since we are comparing Machine Learning Engineer vs Data Scientist, Let’s see who is an ML Engineer.

Who is a Machine Learning Engineer?

Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.

Artificial intelligenceis the goal of a machine learning engineer. They are computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take actions without being specifically directed to perform those tasks.

Machine Learning Engineer vs Data Scientist

A lot of Job posting for Data Scientists emerged and flooded the market during 2012. The same is happening for the Machine Learning Engineer Role, it’s a relatively new one and is slowly emerging at places where we have Data Specialists. The terms are nebulous because they are new. Now, if we compare Machine Learning Engineer vs Data Scientist, there are a few parameters that we need to consider:

Salary Trends

The Average Salary of Data Scientists is around $91,470 (US) or ₹693,637 (IND). Let’s have a look at the Salary of a Data Scientist according to the Experience.

Experience

Salary

Entry Level – IND

₹306,054 – ₹1,215,966

Entry Level – US

$60,894 – $127,894

Experienced – IND

₹972,106 – ₹2,928,194

Experienced – US

$79,321 – $167,947

This figure also depends upon a few other factors like the Company one is working for or the Location. But majorly the above table depicts the average salary range for the different level of experience.

So, if we compare the Salary Trends of Machine Learning Engineer and Data Scientist we can see that in general, a Machine Learning Engineer Earns a little more than a Data Scientist. Now one might ask why is that, so for that, we need to have a look at the skills and the differences in roles between Machine Learning Engineer vs Data Scientist. But first, let’s have a look at the Job Trends.

Job Trends

Data Scientist Job Trends

Location

No. of Jobs

Seattle, WA

2065

New York, NY

1189

San Francisco, CA

1107

Bengaluru, Karnataka

1101

Machine Learning Engineer Job Trends

Location

No. of Jobs

New York, NY

1813

Seattle, WA

1544

San Francisco, CA

1487

Cambridge, MA

936

On one hand, Machine Learning Engineers get slightly more paid than Data Scientist, on the other hand, the demand or the Job openings for a Data Scientist is more than that of an ML Engineer. This is because ML Engineers work on Artificial Intelligence, which is comparatively a new domain.

Skills Requirements

Now the skill requirements for Machine Learning Engineer vs Data Scientist are very similar, so let’s start with the Common Skillsets.

Programming Languages: The first and foremost requirement is to have a good grip on a programming language, preferably python as it is easy to learn and its applications are wider than any other language.

Although Python is a very good Language, it alone cannot help you. You will probably have to learn all these languages like C++, R, Python, Java and also work on MapReduce at some point.

Statistics: Wikipedia defines it as the study of the collection, analysis, interpretation, presentation, and organization of data. Therefore, it shouldn’t be a surprise that Data Scientists, as well as Machine Learning Engineers, need to know statistics. Familiarity with Matrices, Vectors, and Matrix Multiplication is required.

Data Cleaning and Visualization: Data cleansing is a valuable process that can help companies save time and increase their efficiency. Being able to tell a compelling story with data is crucial to getting your point across and keeping your audience engaged.

If your findings can’t be easily and quickly identified, then you’re going to have a difficult time getting through to others. For this reason, data visualization can have a make-or-break effect when it comes to the impact of your data.

Machine Learning and Neural Network Architectures:Machine Learning and predictive modeling are quickly becoming two of the hottest topics. You need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data analytical problems that are based on predictions of major organizational outcomes.

Deep Learning has taken traditional Machine Learning approaches to a next level. It is inspired by biological Neurons (Brain Cells). The idea here is to mimic the human brain. A large network of such Artificial Neurons is used, this is known as Deep Neural Networks.

Big Data Processing Frameworks: A huge amount of data is required to train Machine Learning/ Deep Learning models. Earlier because of the lack of data and computational power, creating precise Machine Learning/ Deep Learning models was not possible. Nowadays a huge amount of data is generated at a good velocity.

Therefore, we require frameworks like Hadoop and Spark to handle Big Data. Nowadays, most organizations are using Big Data analytics to gain hidden business insights. It is, therefore, a must-have skill for a Data Scientist and Machine Learning Engineers.

Industry Knowledge: The most successful Projects out there are going to be those that address real pain points. Whichever industry you’re working for. You should know how that industry works and what will be beneficial for the business. If a Machine Learning Engineer or a Data Scientist does not have business acumen and the know-how of the elements that make up a successful business model, all those technical skills cannot be channeled productively.

You won’t be able to discern the problems and potential challenges that need solving for the business to sustain and grow. You won’t really be able to help your organization explore new business opportunities.

Computer Vision: Computer Vision and Machine Learning are two core branches of Computer Science that can function and power very sophisticated systems that rely on CV and ML algorithms exclusively but when you combine the two, you can achieve even more.

Must-Have Machine Learning Engineer Skills

Language, Audio and Video Processing: Since Natural Language Processing combines two of the major areas of work ie. Linguistics and Computer Science and chances are at some point you’re going to work with either text or audio or video.

So it’s necessary to have good control over libraries like Gensim, NLTK, and techniques like word2vec, sentimental analysis, and summarization.

Applied Mathematics: A lot of machine learning techniques out there are just fancy types of function approximation. Having a firm understanding of Algorithm theory and understanding subjects such as Gradient Descent, Convex Optimizations, Quadratic Programming, and Partial Differentiation will help a lot.

Signal Processing Techniques: One of the few Machine Learning Engineer Skills is also the understanding of Signal Processing and having the ability to solve different problems using Signal Processing techniques as feature extraction is one of the most important parts of Machine Learning.

Knowledge of Time-frequency Analysis and Advanced Signal Processing Algorithms such as Wavelets, Shearlets, Curvelets, and Bandlets will help you to solve complex situations.

Software Development: Machine Learning Engineers are also software developers and are sound in it. It is important to have a good understanding of topics like Data structures, Memory management, and classes. One must know how to package Software, Software Development Life Cycle, Modularity and Design Patterns.

Must-Have Data Scientist Skills

Creative and Critical Thinking: Data Scientists must look at the numbers, trends, and data and come to new conclusions based on the findings. It’s said that smart people ask hard questions while really smart people ask simple ones. Indeed, many of the most important questions you can ask about your company are the simplest. Using data to find answers to your questions means figuring out what to ask in the first place. That can be quite tricky!

Effective Communication: You’ll need to explain a lot of concepts to people with little to no expertise in the field. Chances are you’ll need to work with a team of engineers, as well as many other teams.

Communication is going to make all of this much easier. Companies searching for a strong Data Scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments.

Roles and Responsibilities

Now we come to the final chapter of Machine Learning Engineer vs Data Scientist, ie. what exactly they do in their day to day life and what challenges they face.

Machine Learning Engineer Roles:

Study and transform Data science prototypes

Design Machine Learning Systems

Research and implement appropriate ML algorithms and tools

Develop machine learning applications according to requirements

Select appropriate Datasets and Data Representation Methods

Run Machine Learning Tests and Experiments

Perform Statistical analysis and Fine-Tuning using Test Results

Train and Retrain Systems when Necessary

Extend existing ML Libraries and Frameworks

Keep abreast of Developments in the Field

Data Scientist Roles:

Selecting features, Building and Optimizing Classifiers using Machine Learning Techniques

Understand the customer’s business need and guide them to a solution

Data mining using state-of-the-art methods

Processing, cleansing, and verifying the integrity of data used for analysis

Work with Professional Services DevOps consultants to help customers operationalize models after they are built

Companies Hiring these Professionals

Machine Learning Engineer

Data Scientist

Now, with this, we come to the end of this Machine Learning Engineer vs Data Scientist Article. I hope you got an In-Depth understanding of the two professionals and how they differ in terms of Skillsets, Roles, and Salary.

Edureka’s Python for Data Science Course help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science.

TheMachine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. It also exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.

Got a question for us? Please mention it in the comments section of the “Machine Learning Engineer vs Data Scientist” article and we will get back to you.